The use of deep learning models in computational biology has increased massively in recent years, and is expected to do so further with the current advances in fields like Natural Language Processing. These models, although able to draw complex relations between input and target, are also largely inclined to learn noisy deviations from the pool of data used during their development. In order to assess their performance on unseen data (their capacity to generalize), it is common to randomly split the available data in development (train/validation) and test sets. This procedure, although standard, has lately been shown to produce dubious assessments of generalization due to the existing similarity between samples in the databases used. In this work, we present SpanSeq, a database partition method for machine learning that can scale to most biological sequences (genes, proteins and genomes) in order to avoid data leakage between sets. We also explore the effect of not restraining similarity between sets by reproducing the development of the state-of-the-art model DeepLoc, not only confirming the consequences of randomly splitting databases on the model assessment, but expanding those repercussions to the model development. SpanSeq is available for downloading and installing at https://github.com/genomicepidemiology/SpanSeq.
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